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<li><a class="reference internal" href="#">Color Quantization using K-Means</a></li>
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<div class="sphx-glr-example-title section" id="color-quantization-using-k-means">
<span id="sphx-glr-auto-examples-cluster-plot-color-quantization-py"></span><h1>Color Quantization using K-Means<a class="headerlink" href="#color-quantization-using-k-means" title="Permalink to this headline">¶</a></h1>
<p>Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace
(China), reducing the number of colors required to show the image from 96,615
unique colors to 64, while preserving the overall appearance quality.</p>
<p>In this example, pixels are represented in a 3D-space and K-means is used to
find 64 color clusters. In the image processing literature, the codebook
obtained from K-means (the cluster centers) is called the color palette. Using
a single byte, up to 256 colors can be addressed, whereas an RGB encoding
requires 3 bytes per pixel. The GIF file format, for example, uses such a
palette.</p>
<p>For comparison, a quantized image using a random codebook (colors picked up
randomly) is also shown.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Robert Layton &lt;robertlayton@gmail.com&gt;</span>
<span class="c1">#          Olivier Grisel &lt;olivier.grisel@ensta.org&gt;</span>
<span class="c1">#          Mathieu Blondel &lt;mathieu@mblondel.org&gt;</span>
<span class="c1">#</span>
<span class="c1"># License: BSD 3 clause</span>

<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">pairwise_distances_argmin</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_sample_image</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">shuffle</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>

<span class="n">n_colors</span> <span class="o">=</span> <span class="mi">64</span>

<span class="c1"># Load the Summer Palace photo</span>
<span class="n">china</span> <span class="o">=</span> <span class="n">load_sample_image</span><span class="p">(</span><span class="s2">&quot;china.jpg&quot;</span><span class="p">)</span>

<span class="c1"># Convert to floats instead of the default 8 bits integer coding. Dividing by</span>
<span class="c1"># 255 is important so that plt.imshow behaves works well on float data (need to</span>
<span class="c1"># be in the range [0-1])</span>
<span class="n">china</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">china</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span> <span class="o">/</span> <span class="mi">255</span>

<span class="c1"># Load Image and transform to a 2D numpy array.</span>
<span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">d</span> <span class="o">=</span> <span class="n">original_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">china</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">d</span> <span class="o">==</span> <span class="mi">3</span>
<span class="n">image_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">china</span><span class="p">,</span> <span class="p">(</span><span class="n">w</span> <span class="o">*</span> <span class="n">h</span><span class="p">,</span> <span class="n">d</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fitting model on a small sub-sample of the data&quot;</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">image_array_sample</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">image_array</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)[:</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">n_colors</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">image_array_sample</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;done in </span><span class="si">%0.3f</span><span class="s2">s.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>

<span class="c1"># Get labels for all points</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Predicting color indices on the full image (k-means)&quot;</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">image_array</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;done in </span><span class="si">%0.3f</span><span class="s2">s.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>


<span class="n">codebook_random</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">image_array</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)[:</span><span class="n">n_colors</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Predicting color indices on the full image (random)&quot;</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">labels_random</span> <span class="o">=</span> <span class="n">pairwise_distances_argmin</span><span class="p">(</span><span class="n">codebook_random</span><span class="p">,</span>
                                          <span class="n">image_array</span><span class="p">,</span>
                                          <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;done in </span><span class="si">%0.3f</span><span class="s2">s.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>


<span class="k">def</span> <span class="nf">recreate_image</span><span class="p">(</span><span class="n">codebook</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Recreate the (compressed) image from the code book &amp; labels&quot;&quot;&quot;</span>
    <span class="n">d</span> <span class="o">=</span> <span class="n">codebook</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">d</span><span class="p">))</span>
    <span class="n">label_idx</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">h</span><span class="p">):</span>
            <span class="n">image</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">codebook</span><span class="p">[</span><span class="n">labels</span><span class="p">[</span><span class="n">label_idx</span><span class="p">]]</span>
            <span class="n">label_idx</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">return</span> <span class="n">image</span>

<span class="c1"># Display all results, alongside original image</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">clf</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Original image (96,615 colors)&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">china</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">clf</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Quantized image (64 colors, K-Means)&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">recreate_image</span><span class="p">(</span><span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">clf</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Quantized image (64 colors, Random)&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">recreate_image</span><span class="p">(</span><span class="n">codebook_random</span><span class="p">,</span> <span class="n">labels_random</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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